1. The Soil Moisture Active/Passive (SMAP) Mission: Monitoring Soil Moisture and Freeze/Thaw State John Kimball, Global Vegetation Workshop, June 16-19 2009 NTSG, The University of Montana

2. 2 SMAP Science Objectives Primary Science Objectives: Global, high-resolution mapping of soil moisture and its freeze/thaw state to:  Link terrestrial water, energy and carbon cycle processes  Estimate global water and energy fluxes at the land surface  Quantify net carbon flux in boreal landscapes  Extend weather and climate forecast skill  Develop improved flood and drought prediction capability SMAP is one of the four first-tier missions recommended by the NRC Earth Science Decadal Survey Report Soil moisture and freeze/thaw state are major constraints to land-atmosphere energy, water & carbon exchange Source: Nemani et al. 2003. Science 300

3. 3 SMAP Instrument & Mission Overview Science Measurements  Soil moisture and freeze/thaw state Orbit:  Sun-synchronous, 6 am/6pm equatorial crossing  670 km altitude Instruments:  L-band (1.26 GHz) radar  Polarization: HH, VV, HV  SAR mode: 1-3 km resolution (degrades over center 30% of swath)  Real-aperture mode: 30 x 6 km resolution  L-band (1.4 GHz) radiometer  Polarization: V, H, U  40 km resolution  Instrument antenna (shared by radar & radiometer)  6-m diameter deployable mesh antenna  Conical scan at 14.6 rpm  incidence angle: 40 degrees  Creating Contiguous 1000 km swath  Swath and orbit enable 2-3 day revisit Mission Ops duration: 2013 launch; 3 year baseline SMAP has significant heritage from Hydros ESSP mission concept and Phase A studies

4. 4 “Link Terrestrial Water, Energy and Carbon Cycle Processes” Do Climate Models Correctly Represent the Land surface Control on Water and Energy Fluxes? What Are the Regional Water Cycle Impacts of Climate Variability? Landscape Freeze/Thaw Dynamics Constrain the Boreal Carbon Balance Water and Energy Cycle Soil Moisture Controls the Rate of Continental Water and Energy Cycles Carbon Cycle Are Northern Land Masses Sources or Sinks for Atmospheric Carbon? Surface Soil Moisture [% Volume] Measured by L-Band Radiometer Campbell Yolo Clay Field Experiment Site, California Soil Evaporation Normalized by Potential Evaporation

5. 5 “Estimate Global Water and Energy Fluxes at the Land Surface” Li et al., (2007): Evaluation of IPCC AR4 soil moisture simulations for the second half of the twentieth century, Journal of Geophysical Research, 112. 0 ΔSM ΔT 0 Relative soil moisture changes (%) in IPCC models for scenario from 1960-1999 to 2060-2099 SMAP soil moisture observations will help constrain model parameterizations of surface fluxes and improve model performance IPCC models currently exhibit large differences in soil moisture trends under simulated climate change scenarios Projections of summer soil moisture change (ΔSM) show disagreements in Sign among IPCC AR4 models

6. 6 “Quantify Net Carbon Flux in Boreal Landscapes” Primary thaw day (DOY) McDonald et al. (2004): Variability in springtime thaw in the terrestrial high latitudes: Monitoring a major control on the biospheric assimilation of atmospheric CO 2 with spaceborne microwave remote sensing. Earth Interactions 8(20), 1-23. SMAP will provide important information on environmental constraints to land- atmosphere carbon source/sink dynamics. It will provide more than 8-fold increase in spatial resolution over existing moderate resolution microwave sensors. Thaw day difference from multi-year mean (days) Growing season onset from atmospheric CO2 samples (difference from multi-year mean, days) Annual comparison of pan-Arctic thaw date and high latitude growing season onset inferred from atmospheric CO 2 concentrations, 1988 – 2001 Mean growing season onset for 1988 – 2002 derived from coarse resolution SSM/I data

7. 7 “Extend Weather and Climate Forecast Skill” Predictability of seasonal climate is dependent on boundary conditions such as sea surface temperature (SST) and soil moisture – Soil moisture is particularly important over continental interiors. Difference in Summer Rainfall: 1993 (flood) minus 1988 (drought) years Observations Prediction driven by SST and soil moisture Prediction driven by SST -5 0 +5 Rainfall Difference [mm/day] (Schubert et al., 2002) With Realistic Soil Moisture 24-Hours Ahead High-Resolution Atmospheric Model Forecasts Observed Rainfall 0000Z to 0400Z 13/7/96 (Chen et al., 2001) Buffalo Creek Basin High resolution soil moisture data will improve numerical weather prediction (NWP) over continents by accurately initializing land surface states Without Realistic Soil Moisture

8. 8 “Develop Improved Flood and Drought Prediction Capability” “…delivery of flash-flood guidance to weather forecast offices are centrally dependent on the availability of soil moisture estimates and observations.” “SMAP will provide realistic and reliable soil moisture observations that will potentially open a new era in drought monitoring and decision-support.” Decadal Survey: Current Status: Indirect soil moisture indices are based on rainfall and air temperature (by county or ~30 km) SMAP Capability: Direct soil moisture measurements – global, 3-day, 10 km resolution NOAA National Weather Service Operational Flash Flood Guidance (FFG) Operational Drought Indices Produced by NOAA and National Drought Mitigation Center (NDMC)

9. 9 Satellite Global Biospheric Monitoring & The Problem with Clouds…

10. 10 SMAP Science, Instrument and Mission Requirements SMAP requirements were developed under Hydros and refined through extensive community interaction - The July ’07 NASA SMAP Science Workshop confirmed that these requirements satisfy the SMAP mission science objectives

11. 11 Baseline Science Data Products Data ProductDescription L1B_S0_LoResLow Resolution Radar σ o in Time Order L1C_S0_HiResHigh Resolution Radar σ o on Earth Grid L1B_TBRadiometer T B in Time Order L1C_TBRadiometer T B on Earth Grid L2/3_F/T_HiResFreeze/Thaw State on Earth Grid L2/3_SM_HiResRadar-only Soil Moisture on Earth Grid L2/3_SM_40kmRadiometer-only Soil Moisture on Earth Grid L2/3_SM_A/PRadar/Radiometer Soil Moisture on Earth Grid L4_CarbonCarbon Model Assimilation on Earth Grid L4_SM_profileSoil Moisture Model Assimilation on Earth Grid Global Mapping L-Band Radar and Radiometer High-Resolution and Frequent-Revisit Science Data Observations + Models = Value-Added Science Data

12. 12 SMAP L4_Carbon product: Land-atmosphere CO 2 exchange Motivation/Objectives: Quantify net C flux in boreal landscapes; reduce uncertainty regarding missing C sink on land; Approach: Apply a soil decomposition algorithm driven by SMAP L4_SM and GPP inputs to compute land-atmosphere CO 2 exchange (NEE); Inputs: Daily surface (<5cm) soil moisture & T (L4_SM) & GPP (MODIS/NPP); Outputs: NEE (primary/validated); R eco & SOC (research/optional); Domain: Vegetated areas encompassing boreal/arctic latitudes (≥45°N); Resolution: 10x10 km; Temporal fidelity: Daily (g C m -2 d -1 ); Latency: Initial posting 12 months post-launch, followed by 14-day latency; Accuracy: Commensurate with tower based CO 2 Obs. (RMSE ≤ 30 g C m -2 yr -1 ).

13. 13 Prototype L4_C Product Example NEE for NSA-OBS Ameriflux Site L4_C algorithm using MODIS - AMSR-E inputs BIOME-BGC simulations using local meteorology Tower CO 2 eddy flux measurement results C source (+) C sink (-) L4_C application using MODIS GPP (MOD17) & AMSR-E (SM & T) inputs. The graph (above) shows 2004 seasonal pattern of daily NEE for a mature boreal conifer stand from L4_C, ecosystem model and tower measurements. SMAP L4_C resolution/sampling will allow characterization of surface processes approaching scale & accuracy of tower flux measurements: ~10km resolution, daily repeat, NEE ≤ 30 g C m -2 yr -1 RMSE. NEE (g C m -2 ) DOY 177, 2004 >7 4 2 0 -2 -4 <-7 Mean Daily net CO 2 Exchange (NEE) Source: Kimball et al. 2009 TGARS 47.

14. 14 Global Biophysical Station Networks SMAP Calibration and Validation activities Pre-launch (2009-2013): - Development, testing & selection of baseline algorithms; - Development of algorithm software test-bed for algorithm testing & sensitivity studies; - Verify algorithm sensitivity & accuracy requirements using available satellite, in situ and model based data & targeted field campaigns; - Initialization/calibration/optimization of algorithm parameters (e.g. BPLUT, SOC pools); Post-launch (2013-2015): - Verify product accuracy through focused field campaigns and global observation networks; - Model assimilation based value assessment (GMAO, TOPS, CarbonTracker); Pre-launch L4_C Test using MODIS & AMSR-E Inputs Kimball et al. TGARS 2009

15. 15 http://smap.jpl.nasa.gov/ Opportunities for Community Involvement Community workshops (Events) SMAP SDT Working Groups (Team): - Algorithms - Calibration & Validation - Applications